Population Health Data Implementation Guide (Deliverable 7.1)
From The Embassy of Good Science
ResourcesGuidelines
Population Health Data Implementation Guide (Deliverable 7.1)
Related Initiative
What is this about?
The Population Health Data Implementation Guide (Deliverable 7.1) is part of the WorldFAIR project, which aims to make research data in population health FAIR Findable, Accessible, Interoperable, and Reusable. The guide explains how to describe and share population health data using standard metadata so that both domain experts and broader users can understand and reuse it effectively. It focuses on how to use established models and tools especially the OMOP Common Data Model (CDM) from the OHDSI community to harmonise and structure health datasets. To support access beyond specialist communities like INSPIRE, the guide shows how general metadata standards such as Schema.org can be combined with domain-specific models to describe data resources in a way that is machine-readable and broadly accessible. A significant part of the guide highlights documenting not only the data itself but also the study protocols and analytical processes behind it.
Why is this important?
Ensuring health data is FAIR is essential for better research, policy, and public health outcomes especially when synthesising data from different sources and countries. The guide helps researchers and data managers standardise how data and metadata are described so that others can reliably discover, combine, and reuse health data. By aligning powerful domain-specific models like OMOP CDM with widely accepted metadata standards such as Schema.org, it makes complex datasets understandable and usable outside specialised communities, including by AI tools. This increases the potential for cross-study analysis, global collaborations, and evidence-based decision-making in public health. Ultimately, it supports more consistent, transparent, and interoperable health data ecosystems.
For whom is this important?
Researchers and academicsData managers and research infrastructure teamsPolicy makers and public health institutionsResearch funders and international projectsTechnology developers and AI/data science communities
